Hypergraph construction using Multi-Sensor for helicopter Tail-Drive system fault diagnosis

Abstract

Recently, neural network research in the field of mechanical fault diagnosis often requires a large number of data samples, while graph neural networks constructed using a single sensor under limited samples of the graph topological relationship is difficult to cover the coupling information, and the problem of over-smoothing of the graph can lead to failure of the fault diagnosis. As a result, Multi-Channel Hypergraph Convolutional Network (McHGCN) for limited-sample fault diagnosis of helicopter tail drive system is proposed. Features are extracted from multi-sensor data and hyper edges are constructed by k-nearest neighbor method thus exploiting the similarity. Nodal feature representation of multi-sensor data is done with 1D convolutional network, which prevents over-smoothing while preserving the learning depth. Finally, fault diagnosis is accomplished by semi-supervised learning of the hypergraph convolutional layer. Limited-sample fault diagnosis experiments are conducted on a helicopter tail drive system to verify the effectiveness of the method.